--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - mteb model-index: - name: b1ade_embed_kd results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification config: default split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.81709145427287 - type: ap value: 23.581082591688467 - type: f1 value: 62.54637626017967 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 80.300125 - type: ap value: 74.26836190039964 - type: f1 value: 80.2158066692679 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification config: default split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.084 - type: f1 value: 42.66774553366831 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 29.232000000000003 - type: map_at_10 value: 45.777 - type: map_at_100 value: 46.634 - type: map_at_1000 value: 46.64 - type: map_at_20 value: 46.489000000000004 - type: map_at_3 value: 40.861 - type: map_at_5 value: 43.659 - type: mrr_at_1 value: 30.156 - type: mrr_at_10 value: 46.141 - type: mrr_at_100 value: 46.983999999999995 - type: mrr_at_1000 value: 46.989999999999995 - type: mrr_at_20 value: 46.839 - type: mrr_at_3 value: 41.157 - type: mrr_at_5 value: 44.013000000000005 - type: ndcg_at_1 value: 29.232000000000003 - type: ndcg_at_10 value: 54.832 - type: ndcg_at_100 value: 58.303000000000004 - type: ndcg_at_1000 value: 58.451 - type: ndcg_at_20 value: 57.328 - type: ndcg_at_3 value: 44.685 - type: ndcg_at_5 value: 49.756 - type: precision_at_1 value: 29.232000000000003 - type: precision_at_10 value: 8.371 - type: precision_at_100 value: 0.985 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.6690000000000005 - type: precision_at_3 value: 18.587 - type: precision_at_5 value: 13.627 - type: recall_at_1 value: 29.232000000000003 - type: recall_at_10 value: 83.71300000000001 - type: recall_at_100 value: 98.506 - type: recall_at_1000 value: 99.644 - type: recall_at_20 value: 93.38499999999999 - type: recall_at_3 value: 55.761 - type: recall_at_5 value: 68.137 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.801946024895756 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 37.639210206045206 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 57.589359041891576 - type: mrr value: 70.88334872268389 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 86.63594177060354 - type: cos_sim_spearman value: 84.75132870687939 - type: euclidean_pearson value: 85.05646621990854 - type: euclidean_spearman value: 84.68686940680522 - type: manhattan_pearson value: 85.08705700579426 - type: manhattan_spearman value: 84.83446313127413 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.1948051948052 - type: f1 value: 85.13229898343104 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.68616898014911 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.45376891835619 - task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 26.340000000000003 - type: map_at_10 value: 36.513 - type: map_at_100 value: 37.968 - type: map_at_1000 value: 38.107 - type: map_at_20 value: 37.355 - type: map_at_3 value: 33.153 - type: map_at_5 value: 34.899 - type: mrr_at_1 value: 33.763 - type: mrr_at_10 value: 42.778 - type: mrr_at_100 value: 43.667 - type: mrr_at_1000 value: 43.724000000000004 - type: mrr_at_20 value: 43.349 - type: mrr_at_3 value: 40.32 - type: mrr_at_5 value: 41.657 - type: ndcg_at_1 value: 33.763 - type: ndcg_at_10 value: 42.783 - type: ndcg_at_100 value: 48.209999999999994 - type: ndcg_at_1000 value: 50.678999999999995 - type: ndcg_at_20 value: 45.073 - type: ndcg_at_3 value: 37.841 - type: ndcg_at_5 value: 39.818999999999996 - type: precision_at_1 value: 33.763 - type: precision_at_10 value: 8.398 - type: precision_at_100 value: 1.396 - type: precision_at_1000 value: 0.188 - type: precision_at_20 value: 5.0569999999999995 - type: precision_at_3 value: 18.503 - type: precision_at_5 value: 13.219 - type: recall_at_1 value: 26.340000000000003 - type: recall_at_10 value: 54.603 - type: recall_at_100 value: 77.264 - type: recall_at_1000 value: 93.882 - type: recall_at_20 value: 63.101 - type: recall_at_3 value: 39.6 - type: recall_at_5 value: 45.651 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 24.313000000000002 - type: map_at_10 value: 33.225 - type: map_at_100 value: 34.293 - type: map_at_1000 value: 34.421 - type: map_at_20 value: 33.818 - type: map_at_3 value: 30.545 - type: map_at_5 value: 32.144 - type: mrr_at_1 value: 31.083 - type: mrr_at_10 value: 39.199 - type: mrr_at_100 value: 39.835 - type: mrr_at_1000 value: 39.892 - type: mrr_at_20 value: 39.57 - type: mrr_at_3 value: 36.879 - type: mrr_at_5 value: 38.245000000000005 - type: ndcg_at_1 value: 31.083 - type: ndcg_at_10 value: 38.553 - type: ndcg_at_100 value: 42.685 - type: ndcg_at_1000 value: 45.144 - type: ndcg_at_20 value: 40.116 - type: ndcg_at_3 value: 34.608 - type: ndcg_at_5 value: 36.551 - type: precision_at_1 value: 31.083 - type: precision_at_10 value: 7.28 - type: precision_at_100 value: 1.183 - type: precision_at_1000 value: 0.168 - type: precision_at_20 value: 4.322 - type: precision_at_3 value: 16.858 - type: precision_at_5 value: 12.127 - type: recall_at_1 value: 24.313000000000002 - type: recall_at_10 value: 48.117 - type: recall_at_100 value: 65.768 - type: recall_at_1000 value: 81.935 - type: recall_at_20 value: 53.689 - type: recall_at_3 value: 36.335 - type: recall_at_5 value: 41.803000000000004 - task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 33.013999999999996 - type: map_at_10 value: 44.567 - type: map_at_100 value: 45.664 - type: map_at_1000 value: 45.732 - type: map_at_20 value: 45.190000000000005 - type: map_at_3 value: 41.393 - type: map_at_5 value: 43.147000000000006 - type: mrr_at_1 value: 37.806 - type: mrr_at_10 value: 47.841 - type: mrr_at_100 value: 48.597 - type: mrr_at_1000 value: 48.638 - type: mrr_at_20 value: 48.262 - type: mrr_at_3 value: 45.361000000000004 - type: mrr_at_5 value: 46.803 - type: ndcg_at_1 value: 37.806 - type: ndcg_at_10 value: 50.412 - type: ndcg_at_100 value: 55.019 - type: ndcg_at_1000 value: 56.52 - type: ndcg_at_20 value: 52.23100000000001 - type: ndcg_at_3 value: 44.944 - type: ndcg_at_5 value: 47.535 - type: precision_at_1 value: 37.806 - type: precision_at_10 value: 8.351 - type: precision_at_100 value: 1.163 - type: precision_at_1000 value: 0.134 - type: precision_at_20 value: 4.727 - type: precision_at_3 value: 20.376 - type: precision_at_5 value: 14.056 - type: recall_at_1 value: 33.013999999999996 - type: recall_at_10 value: 64.35600000000001 - type: recall_at_100 value: 84.748 - type: recall_at_1000 value: 95.525 - type: recall_at_20 value: 71.137 - type: recall_at_3 value: 49.726 - type: recall_at_5 value: 56.054 - task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 18.476 - type: map_at_10 value: 24.715999999999998 - type: map_at_100 value: 25.72 - type: map_at_1000 value: 25.826999999999998 - type: map_at_20 value: 25.276 - type: map_at_3 value: 22.656000000000002 - type: map_at_5 value: 23.737 - type: mrr_at_1 value: 20.113 - type: mrr_at_10 value: 26.423999999999996 - type: mrr_at_100 value: 27.328000000000003 - type: mrr_at_1000 value: 27.418 - type: mrr_at_20 value: 26.936 - type: mrr_at_3 value: 24.275 - type: mrr_at_5 value: 25.501 - type: ndcg_at_1 value: 20.113 - type: ndcg_at_10 value: 28.626 - type: ndcg_at_100 value: 33.649 - type: ndcg_at_1000 value: 36.472 - type: ndcg_at_20 value: 30.581999999999997 - type: ndcg_at_3 value: 24.490000000000002 - type: ndcg_at_5 value: 26.394000000000002 - type: precision_at_1 value: 20.113 - type: precision_at_10 value: 4.52 - type: precision_at_100 value: 0.739 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_20 value: 2.706 - type: precision_at_3 value: 10.433 - type: precision_at_5 value: 7.48 - type: recall_at_1 value: 18.476 - type: recall_at_10 value: 39.129000000000005 - type: recall_at_100 value: 62.44 - type: recall_at_1000 value: 83.95700000000001 - type: recall_at_20 value: 46.611999999999995 - type: recall_at_3 value: 27.772000000000002 - type: recall_at_5 value: 32.312000000000005 - task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 10.126 - type: map_at_10 value: 15.916 - type: map_at_100 value: 17.049 - type: map_at_1000 value: 17.19 - type: map_at_20 value: 16.569 - type: map_at_3 value: 13.986 - type: map_at_5 value: 15.052999999999999 - type: mrr_at_1 value: 13.059999999999999 - type: mrr_at_10 value: 19.52 - type: mrr_at_100 value: 20.599999999999998 - type: mrr_at_1000 value: 20.693 - type: mrr_at_20 value: 20.177999999999997 - type: mrr_at_3 value: 17.496000000000002 - type: mrr_at_5 value: 18.541 - type: ndcg_at_1 value: 13.059999999999999 - type: ndcg_at_10 value: 19.987 - type: ndcg_at_100 value: 25.602000000000004 - type: ndcg_at_1000 value: 29.171999999999997 - type: ndcg_at_20 value: 22.31 - type: ndcg_at_3 value: 16.286 - type: ndcg_at_5 value: 17.931 - type: precision_at_1 value: 13.059999999999999 - type: precision_at_10 value: 3.9050000000000002 - type: precision_at_100 value: 0.771 - type: precision_at_1000 value: 0.123 - type: precision_at_20 value: 2.606 - type: precision_at_3 value: 8.167 - type: precision_at_5 value: 6.045 - type: recall_at_1 value: 10.126 - type: recall_at_10 value: 29.137 - type: recall_at_100 value: 53.824000000000005 - type: recall_at_1000 value: 79.373 - type: recall_at_20 value: 37.475 - type: recall_at_3 value: 18.791 - type: recall_at_5 value: 22.993 - task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 25.281 - type: map_at_10 value: 34.875 - type: map_at_100 value: 36.268 - type: map_at_1000 value: 36.385 - type: map_at_20 value: 35.711999999999996 - type: map_at_3 value: 31.808999999999997 - type: map_at_5 value: 33.550999999999995 - type: mrr_at_1 value: 31.28 - type: mrr_at_10 value: 40.489000000000004 - type: mrr_at_100 value: 41.434 - type: mrr_at_1000 value: 41.491 - type: mrr_at_20 value: 41.088 - type: mrr_at_3 value: 38.033 - type: mrr_at_5 value: 39.621 - type: ndcg_at_1 value: 31.28 - type: ndcg_at_10 value: 40.716 - type: ndcg_at_100 value: 46.45 - type: ndcg_at_1000 value: 48.851 - type: ndcg_at_20 value: 43.216 - type: ndcg_at_3 value: 35.845 - type: ndcg_at_5 value: 38.251000000000005 - type: precision_at_1 value: 31.28 - type: precision_at_10 value: 7.623 - type: precision_at_100 value: 1.214 - type: precision_at_1000 value: 0.159 - type: precision_at_20 value: 4.625 - type: precision_at_3 value: 17.26 - type: precision_at_5 value: 12.435 - type: recall_at_1 value: 25.281 - type: recall_at_10 value: 52.476 - type: recall_at_100 value: 76.535 - type: recall_at_1000 value: 92.658 - type: recall_at_20 value: 61.211000000000006 - type: recall_at_3 value: 38.805 - type: recall_at_5 value: 45.053 - task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 20.092 - type: map_at_10 value: 27.805999999999997 - type: map_at_100 value: 29.137999999999998 - type: map_at_1000 value: 29.266 - type: map_at_20 value: 28.587 - type: map_at_3 value: 25.112000000000002 - type: map_at_5 value: 26.551000000000002 - type: mrr_at_1 value: 24.315 - type: mrr_at_10 value: 32.068000000000005 - type: mrr_at_100 value: 33.039 - type: mrr_at_1000 value: 33.114 - type: mrr_at_20 value: 32.66 - type: mrr_at_3 value: 29.49 - type: mrr_at_5 value: 30.906 - type: ndcg_at_1 value: 24.315 - type: ndcg_at_10 value: 32.9 - type: ndcg_at_100 value: 38.741 - type: ndcg_at_1000 value: 41.657 - type: ndcg_at_20 value: 35.338 - type: ndcg_at_3 value: 28.069 - type: ndcg_at_5 value: 30.169 - type: precision_at_1 value: 24.315 - type: precision_at_10 value: 6.2330000000000005 - type: precision_at_100 value: 1.072 - type: precision_at_1000 value: 0.15 - type: precision_at_20 value: 3.8580000000000005 - type: precision_at_3 value: 13.318 - type: precision_at_5 value: 9.748999999999999 - type: recall_at_1 value: 20.092 - type: recall_at_10 value: 43.832 - type: recall_at_100 value: 68.75099999999999 - type: recall_at_1000 value: 89.25 - type: recall_at_20 value: 52.445 - type: recall_at_3 value: 30.666 - type: recall_at_5 value: 35.873 - task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 19.317 - type: map_at_10 value: 26.653 - type: map_at_100 value: 28.011999999999997 - type: map_at_1000 value: 28.231 - type: map_at_20 value: 27.301 - type: map_at_3 value: 23.763 - type: map_at_5 value: 25.391000000000002 - type: mrr_at_1 value: 24.506 - type: mrr_at_10 value: 31.991999999999997 - type: mrr_at_100 value: 32.924 - type: mrr_at_1000 value: 32.993 - type: mrr_at_20 value: 32.521 - type: mrr_at_3 value: 29.48 - type: mrr_at_5 value: 30.982 - type: ndcg_at_1 value: 24.506 - type: ndcg_at_10 value: 32.202999999999996 - type: ndcg_at_100 value: 37.797 - type: ndcg_at_1000 value: 40.859 - type: ndcg_at_20 value: 34.098 - type: ndcg_at_3 value: 27.552 - type: ndcg_at_5 value: 29.781000000000002 - type: precision_at_1 value: 24.506 - type: precision_at_10 value: 6.462 - type: precision_at_100 value: 1.35 - type: precision_at_1000 value: 0.22499999999999998 - type: precision_at_20 value: 4.071000000000001 - type: precision_at_3 value: 13.241 - type: precision_at_5 value: 9.921000000000001 - type: recall_at_1 value: 19.317 - type: recall_at_10 value: 42.296 - type: recall_at_100 value: 68.2 - type: recall_at_1000 value: 88.565 - type: recall_at_20 value: 49.883 - type: recall_at_3 value: 28.608 - type: recall_at_5 value: 34.854 - task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 18.0 - type: map_at_10 value: 24.444 - type: map_at_100 value: 25.205 - type: map_at_1000 value: 25.291000000000004 - type: map_at_20 value: 24.834 - type: map_at_3 value: 22.311 - type: map_at_5 value: 23.442 - type: mrr_at_1 value: 20.552 - type: mrr_at_10 value: 27.028999999999996 - type: mrr_at_100 value: 27.706999999999997 - type: mrr_at_1000 value: 27.775 - type: mrr_at_20 value: 27.366 - type: mrr_at_3 value: 25.051000000000002 - type: mrr_at_5 value: 26.063 - type: ndcg_at_1 value: 20.552 - type: ndcg_at_10 value: 28.519 - type: ndcg_at_100 value: 32.580999999999996 - type: ndcg_at_1000 value: 34.99 - type: ndcg_at_20 value: 29.848000000000003 - type: ndcg_at_3 value: 24.46 - type: ndcg_at_5 value: 26.273000000000003 - type: precision_at_1 value: 20.552 - type: precision_at_10 value: 4.801 - type: precision_at_100 value: 0.729 - type: precision_at_1000 value: 0.101 - type: precision_at_20 value: 2.715 - type: precision_at_3 value: 10.940999999999999 - type: precision_at_5 value: 7.761 - type: recall_at_1 value: 18.0 - type: recall_at_10 value: 38.425 - type: recall_at_100 value: 57.885 - type: recall_at_1000 value: 75.945 - type: recall_at_20 value: 43.472 - type: recall_at_3 value: 27.483 - type: recall_at_5 value: 31.866 - task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 10.014000000000001 - type: map_at_10 value: 14.462 - type: map_at_100 value: 15.364 - type: map_at_1000 value: 15.482999999999999 - type: map_at_20 value: 14.931 - type: map_at_3 value: 12.842 - type: map_at_5 value: 13.697999999999999 - type: mrr_at_1 value: 12.526000000000002 - type: mrr_at_10 value: 17.433 - type: mrr_at_100 value: 18.296 - type: mrr_at_1000 value: 18.383 - type: mrr_at_20 value: 17.897 - type: mrr_at_3 value: 15.703 - type: mrr_at_5 value: 16.627 - type: ndcg_at_1 value: 12.526000000000002 - type: ndcg_at_10 value: 17.697 - type: ndcg_at_100 value: 22.33 - type: ndcg_at_1000 value: 25.587 - type: ndcg_at_20 value: 19.302 - type: ndcg_at_3 value: 14.606 - type: ndcg_at_5 value: 15.946 - type: precision_at_1 value: 12.526000000000002 - type: precision_at_10 value: 3.383 - type: precision_at_100 value: 0.6799999999999999 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_20 value: 2.147 - type: precision_at_3 value: 7.02 - type: precision_at_5 value: 5.196 - type: recall_at_1 value: 10.014000000000001 - type: recall_at_10 value: 24.623 - type: recall_at_100 value: 45.795 - type: recall_at_1000 value: 69.904 - type: recall_at_20 value: 30.534 - type: recall_at_3 value: 15.955 - type: recall_at_5 value: 19.394 - task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 19.156000000000002 - type: map_at_10 value: 26.144000000000002 - type: map_at_100 value: 27.157999999999998 - type: map_at_1000 value: 27.288 - type: map_at_20 value: 26.689 - type: map_at_3 value: 24.125 - type: map_at_5 value: 25.369000000000003 - type: mrr_at_1 value: 22.854 - type: mrr_at_10 value: 29.874000000000002 - type: mrr_at_100 value: 30.738 - type: mrr_at_1000 value: 30.826999999999998 - type: mrr_at_20 value: 30.354 - type: mrr_at_3 value: 27.689999999999998 - type: mrr_at_5 value: 29.131 - type: ndcg_at_1 value: 22.854 - type: ndcg_at_10 value: 30.469 - type: ndcg_at_100 value: 35.475 - type: ndcg_at_1000 value: 38.59 - type: ndcg_at_20 value: 32.333 - type: ndcg_at_3 value: 26.674999999999997 - type: ndcg_at_5 value: 28.707 - type: precision_at_1 value: 22.854 - type: precision_at_10 value: 5.1209999999999996 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.123 - type: precision_at_20 value: 3.0460000000000003 - type: precision_at_3 value: 12.127 - type: precision_at_5 value: 8.75 - type: recall_at_1 value: 19.156000000000002 - type: recall_at_10 value: 40.009 - type: recall_at_100 value: 62.419999999999995 - type: recall_at_1000 value: 84.585 - type: recall_at_20 value: 46.912 - type: recall_at_3 value: 29.733999999999998 - type: recall_at_5 value: 34.741 - task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 19.317 - type: map_at_10 value: 26.653 - type: map_at_100 value: 28.011999999999997 - type: map_at_1000 value: 28.231 - type: map_at_20 value: 27.301 - type: map_at_3 value: 23.763 - type: map_at_5 value: 25.391000000000002 - type: mrr_at_1 value: 24.506 - type: mrr_at_10 value: 31.991999999999997 - type: mrr_at_100 value: 32.924 - type: mrr_at_1000 value: 32.993 - type: mrr_at_20 value: 32.521 - type: mrr_at_3 value: 29.48 - type: mrr_at_5 value: 30.982 - type: ndcg_at_1 value: 24.506 - type: ndcg_at_10 value: 32.202999999999996 - type: ndcg_at_100 value: 37.797 - type: ndcg_at_1000 value: 40.859 - type: ndcg_at_20 value: 34.098 - type: ndcg_at_3 value: 27.552 - type: ndcg_at_5 value: 29.781000000000002 - type: precision_at_1 value: 24.506 - type: precision_at_10 value: 6.462 - type: precision_at_100 value: 1.35 - type: precision_at_1000 value: 0.22499999999999998 - type: precision_at_20 value: 4.071000000000001 - type: precision_at_3 value: 13.241 - type: precision_at_5 value: 9.921000000000001 - type: recall_at_1 value: 19.317 - type: recall_at_10 value: 42.296 - type: recall_at_100 value: 68.2 - type: recall_at_1000 value: 88.565 - type: recall_at_20 value: 49.883 - type: recall_at_3 value: 28.608 - type: recall_at_5 value: 34.854 - task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.822 - type: map_at_10 value: 18.055 - type: map_at_100 value: 18.942 - type: map_at_1000 value: 19.057 - type: map_at_20 value: 18.544 - type: map_at_3 value: 15.964 - type: map_at_5 value: 16.833000000000002 - type: mrr_at_1 value: 14.048 - type: mrr_at_10 value: 19.489 - type: mrr_at_100 value: 20.392 - type: mrr_at_1000 value: 20.49 - type: mrr_at_20 value: 19.979 - type: mrr_at_3 value: 17.344 - type: mrr_at_5 value: 18.287 - type: ndcg_at_1 value: 14.048 - type: ndcg_at_10 value: 21.737000000000002 - type: ndcg_at_100 value: 26.383000000000003 - type: ndcg_at_1000 value: 29.555 - type: ndcg_at_20 value: 23.463 - type: ndcg_at_3 value: 17.29 - type: ndcg_at_5 value: 18.829 - type: precision_at_1 value: 14.048 - type: precision_at_10 value: 3.6229999999999998 - type: precision_at_100 value: 0.641 - type: precision_at_1000 value: 0.099 - type: precision_at_20 value: 2.1999999999999997 - type: precision_at_3 value: 7.2090000000000005 - type: precision_at_5 value: 5.213 - type: recall_at_1 value: 12.822 - type: recall_at_10 value: 32.123000000000005 - type: recall_at_100 value: 53.657999999999994 - type: recall_at_1000 value: 77.72200000000001 - type: recall_at_20 value: 38.66 - type: recall_at_3 value: 19.814999999999998 - type: recall_at_5 value: 23.432 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 13.119 - type: map_at_10 value: 22.999 - type: map_at_100 value: 25.108000000000004 - type: map_at_1000 value: 25.306 - type: map_at_20 value: 24.141000000000002 - type: map_at_3 value: 19.223000000000003 - type: map_at_5 value: 21.181 - type: mrr_at_1 value: 30.554 - type: mrr_at_10 value: 42.553000000000004 - type: mrr_at_100 value: 43.498 - type: mrr_at_1000 value: 43.527 - type: mrr_at_20 value: 43.193 - type: mrr_at_3 value: 39.283 - type: mrr_at_5 value: 41.143 - type: ndcg_at_1 value: 30.554 - type: ndcg_at_10 value: 31.946 - type: ndcg_at_100 value: 39.934999999999995 - type: ndcg_at_1000 value: 43.256 - type: ndcg_at_20 value: 35.101 - type: ndcg_at_3 value: 26.489 - type: ndcg_at_5 value: 28.272000000000002 - type: precision_at_1 value: 30.554 - type: precision_at_10 value: 10.039 - type: precision_at_100 value: 1.864 - type: precision_at_1000 value: 0.248 - type: precision_at_20 value: 6.371 - type: precision_at_3 value: 20.174 - type: precision_at_5 value: 15.296000000000001 - type: recall_at_1 value: 13.119 - type: recall_at_10 value: 37.822 - type: recall_at_100 value: 65.312 - type: recall_at_1000 value: 83.817 - type: recall_at_20 value: 46.760000000000005 - type: recall_at_3 value: 23.858999999999998 - type: recall_at_5 value: 29.609999999999996 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 8.176 - type: map_at_10 value: 19.594 - type: map_at_100 value: 28.081 - type: map_at_1000 value: 29.864 - type: map_at_20 value: 22.983999999999998 - type: map_at_3 value: 13.923 - type: map_at_5 value: 16.597 - type: mrr_at_1 value: 66.75 - type: mrr_at_10 value: 75.82600000000001 - type: mrr_at_100 value: 76.145 - type: mrr_at_1000 value: 76.14999999999999 - type: mrr_at_20 value: 76.074 - type: mrr_at_3 value: 74.333 - type: mrr_at_5 value: 75.25800000000001 - type: ndcg_at_1 value: 54.50000000000001 - type: ndcg_at_10 value: 41.806 - type: ndcg_at_100 value: 47.067 - type: ndcg_at_1000 value: 54.397 - type: ndcg_at_20 value: 41.727 - type: ndcg_at_3 value: 46.92 - type: ndcg_at_5 value: 44.381 - type: precision_at_1 value: 66.75 - type: precision_at_10 value: 33.35 - type: precision_at_100 value: 10.92 - type: precision_at_1000 value: 2.222 - type: precision_at_20 value: 25.862000000000002 - type: precision_at_3 value: 51.417 - type: precision_at_5 value: 43.65 - type: recall_at_1 value: 8.176 - type: recall_at_10 value: 26.029000000000003 - type: recall_at_100 value: 53.872 - type: recall_at_1000 value: 76.895 - type: recall_at_20 value: 34.192 - type: recall_at_3 value: 15.789 - type: recall_at_5 value: 20.255000000000003 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.22 - type: f1 value: 43.59074485488622 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 40.872 - type: map_at_10 value: 55.178000000000004 - type: map_at_100 value: 55.859 - type: map_at_1000 value: 55.881 - type: map_at_20 value: 55.66 - type: map_at_3 value: 51.4 - type: map_at_5 value: 53.754000000000005 - type: mrr_at_1 value: 43.744 - type: mrr_at_10 value: 58.36900000000001 - type: mrr_at_100 value: 58.911 - type: mrr_at_1000 value: 58.916999999999994 - type: mrr_at_20 value: 58.779 - type: mrr_at_3 value: 54.653 - type: mrr_at_5 value: 56.987 - type: ndcg_at_1 value: 43.744 - type: ndcg_at_10 value: 62.936 - type: ndcg_at_100 value: 65.666 - type: ndcg_at_1000 value: 66.08699999999999 - type: ndcg_at_20 value: 64.548 - type: ndcg_at_3 value: 55.543 - type: ndcg_at_5 value: 59.646 - type: precision_at_1 value: 43.744 - type: precision_at_10 value: 9.191 - type: precision_at_100 value: 1.072 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_20 value: 4.967 - type: precision_at_3 value: 23.157 - type: precision_at_5 value: 16.115 - type: recall_at_1 value: 40.872 - type: recall_at_10 value: 83.818 - type: recall_at_100 value: 95.14200000000001 - type: recall_at_1000 value: 97.897 - type: recall_at_20 value: 89.864 - type: recall_at_3 value: 64.19200000000001 - type: recall_at_5 value: 74.029 - task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 14.804999999999998 - type: map_at_10 value: 22.86 - type: map_at_100 value: 24.823999999999998 - type: map_at_1000 value: 25.041000000000004 - type: map_at_20 value: 23.881 - type: map_at_3 value: 20.09 - type: map_at_5 value: 21.39 - type: mrr_at_1 value: 29.938 - type: mrr_at_10 value: 37.041000000000004 - type: mrr_at_100 value: 38.196000000000005 - type: mrr_at_1000 value: 38.256 - type: mrr_at_20 value: 37.693 - type: mrr_at_3 value: 34.721999999999994 - type: mrr_at_5 value: 35.787 - type: ndcg_at_1 value: 29.938 - type: ndcg_at_10 value: 29.358 - type: ndcg_at_100 value: 37.544 - type: ndcg_at_1000 value: 41.499 - type: ndcg_at_20 value: 32.354 - type: ndcg_at_3 value: 26.434 - type: ndcg_at_5 value: 26.93 - type: precision_at_1 value: 29.938 - type: precision_at_10 value: 8.117 - type: precision_at_100 value: 1.611 - type: precision_at_1000 value: 0.232 - type: precision_at_20 value: 5.255 - type: precision_at_3 value: 17.49 - type: precision_at_5 value: 12.747 - type: recall_at_1 value: 14.804999999999998 - type: recall_at_10 value: 34.776 - type: recall_at_100 value: 66.279 - type: recall_at_1000 value: 89.96600000000001 - type: recall_at_20 value: 44.31 - type: recall_at_3 value: 23.623 - type: recall_at_5 value: 27.194000000000003 - task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 38.555 - type: map_at_10 value: 54.20700000000001 - type: map_at_100 value: 55.177 - type: map_at_1000 value: 55.254999999999995 - type: map_at_20 value: 54.788000000000004 - type: map_at_3 value: 51.034 - type: map_at_5 value: 52.998 - type: mrr_at_1 value: 77.11 - type: mrr_at_10 value: 82.93199999999999 - type: mrr_at_100 value: 83.14200000000001 - type: mrr_at_1000 value: 83.15 - type: mrr_at_20 value: 83.062 - type: mrr_at_3 value: 81.95599999999999 - type: mrr_at_5 value: 82.586 - type: ndcg_at_1 value: 77.11 - type: ndcg_at_10 value: 63.853 - type: ndcg_at_100 value: 67.18499999999999 - type: ndcg_at_1000 value: 68.676 - type: ndcg_at_20 value: 65.279 - type: ndcg_at_3 value: 59.301 - type: ndcg_at_5 value: 61.822 - type: precision_at_1 value: 77.11 - type: precision_at_10 value: 13.044 - type: precision_at_100 value: 1.5630000000000002 - type: precision_at_1000 value: 0.17600000000000002 - type: precision_at_20 value: 6.979 - type: precision_at_3 value: 36.759 - type: precision_at_5 value: 24.054000000000002 - type: recall_at_1 value: 38.555 - type: recall_at_10 value: 65.21900000000001 - type: recall_at_100 value: 78.16300000000001 - type: recall_at_1000 value: 88.02799999999999 - type: recall_at_20 value: 69.791 - type: recall_at_3 value: 55.138 - type: recall_at_5 value: 60.135000000000005 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 69.8728 - type: ap value: 63.98214492125858 - type: f1 value: 69.59975497754624 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification config: default split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.76288189694483 - type: f1 value: 94.52150972672682 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification config: default split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.83994528043777 - type: f1 value: 57.95571154189732 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification config: default split: test revision: 4672e20407010da34463acc759c162ca9734bca6 metrics: - type: accuracy value: 46.1163416274378 - type: f1 value: 45.425692244093064 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification config: default split: test revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 metrics: - type: accuracy value: 45.57834566240753 - type: f1 value: 43.84840097785479 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 32.86396397182615 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 34.018965727588565 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7 metrics: - type: map value: 31.286618059824573 - type: mrr value: 32.481830769278965 - task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.236 - type: map_at_10 value: 9.352 - type: map_at_100 value: 12.382 - type: map_at_1000 value: 13.828999999999999 - type: map_at_20 value: 10.619 - type: map_at_3 value: 6.814000000000001 - type: map_at_5 value: 7.887 - type: mrr_at_1 value: 37.152 - type: mrr_at_10 value: 47.055 - type: mrr_at_100 value: 47.82 - type: mrr_at_1000 value: 47.86 - type: mrr_at_20 value: 47.605 - type: mrr_at_3 value: 44.118 - type: mrr_at_5 value: 46.115 - type: ndcg_at_1 value: 34.365 - type: ndcg_at_10 value: 28.473 - type: ndcg_at_100 value: 27.311999999999998 - type: ndcg_at_1000 value: 36.671 - type: ndcg_at_20 value: 27.137 - type: ndcg_at_3 value: 31.939 - type: ndcg_at_5 value: 30.428 - type: precision_at_1 value: 36.223 - type: precision_at_10 value: 21.858 - type: precision_at_100 value: 7.417999999999999 - type: precision_at_1000 value: 2.0709999999999997 - type: precision_at_20 value: 16.502 - type: precision_at_3 value: 30.857 - type: precision_at_5 value: 26.997 - type: recall_at_1 value: 4.236 - type: recall_at_10 value: 13.489 - type: recall_at_100 value: 29.580000000000002 - type: recall_at_1000 value: 62.726000000000006 - type: recall_at_20 value: 18.346999999999998 - type: recall_at_3 value: 7.811 - type: recall_at_5 value: 10.086 - task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 21.123 - type: map_at_10 value: 34.429 - type: map_at_100 value: 35.803000000000004 - type: map_at_1000 value: 35.853 - type: map_at_20 value: 35.308 - type: map_at_3 value: 30.095 - type: map_at_5 value: 32.435 - type: mrr_at_1 value: 23.841 - type: mrr_at_10 value: 36.864999999999995 - type: mrr_at_100 value: 37.935 - type: mrr_at_1000 value: 37.97 - type: mrr_at_20 value: 37.566 - type: mrr_at_3 value: 32.918 - type: mrr_at_5 value: 35.11 - type: ndcg_at_1 value: 23.841 - type: ndcg_at_10 value: 42.043 - type: ndcg_at_100 value: 48.015 - type: ndcg_at_1000 value: 49.152 - type: ndcg_at_20 value: 44.936 - type: ndcg_at_3 value: 33.513999999999996 - type: ndcg_at_5 value: 37.541999999999994 - type: precision_at_1 value: 23.841 - type: precision_at_10 value: 7.454 - type: precision_at_100 value: 1.081 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_20 value: 4.413 - type: precision_at_3 value: 15.672 - type: precision_at_5 value: 11.657 - type: recall_at_1 value: 21.123 - type: recall_at_10 value: 63.096 - type: recall_at_100 value: 89.27199999999999 - type: recall_at_1000 value: 97.69 - type: recall_at_20 value: 73.873 - type: recall_at_3 value: 40.588 - type: recall_at_5 value: 49.928 - task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 metrics: - type: map_at_1 value: 70.255 - type: map_at_10 value: 84.387 - type: map_at_100 value: 85.027 - type: map_at_1000 value: 85.043 - type: map_at_20 value: 84.809 - type: map_at_3 value: 81.5 - type: map_at_5 value: 83.286 - type: mrr_at_1 value: 80.85 - type: mrr_at_10 value: 87.25699999999999 - type: mrr_at_100 value: 87.363 - type: mrr_at_1000 value: 87.363 - type: mrr_at_20 value: 87.336 - type: mrr_at_3 value: 86.357 - type: mrr_at_5 value: 86.939 - type: ndcg_at_1 value: 80.86 - type: ndcg_at_10 value: 88.151 - type: ndcg_at_100 value: 89.381 - type: ndcg_at_1000 value: 89.47800000000001 - type: ndcg_at_20 value: 88.82100000000001 - type: ndcg_at_3 value: 85.394 - type: ndcg_at_5 value: 86.855 - type: precision_at_1 value: 80.86 - type: precision_at_10 value: 13.397 - 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type: cos_sim_pearson value: 31.440481026661672 - type: cos_sim_spearman value: 31.592743544965913 - type: euclidean_pearson value: 31.15111049327518 - type: euclidean_spearman value: 30.555124184361464 - type: manhattan_pearson value: 31.724139249295654 - type: manhattan_spearman value: 30.483389245793504 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 config: default split: test revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 metrics: - type: cos_sim_pearson value: 34.51489724275415 - type: cos_sim_spearman value: 47.06532141601629 - type: euclidean_pearson value: 33.28904737503036 - type: euclidean_spearman value: 45.111172981641865 - type: manhattan_pearson value: 33.36374172942392 - type: manhattan_spearman value: 45.100940945158534 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 82.09996292950329 - type: cos_sim_spearman value: 82.69376206796092 - 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type: max_f1 value: 76.54849595413475 --- # b1ade-embed-kd This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was distilled with teacher model as and student model as b1ade-embed **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 275105 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MSELoss.MSELoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 5000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "", "optimizer_params": { "eps": 1e-06, "lr": 5e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Results: Good agreement with teacher model, at least on STS: Teacher: ``` 2024-05-20 16:29:07 - Teacher Performance: 2024-05-20 16:29:07 - EmbeddingSimilarityEvaluator: Evaluating the model on the sts-dev dataset: 2024-05-20 16:29:12 - Cosine-Similarity : Pearson: 0.8561 Spearman: 0.8597 2024-05-20 16:29:12 - Manhattan-Distance: Pearson: 0.8569 Spearman: 0.8567 2024-05-20 16:29:12 - Euclidean-Distance: Pearson: 0.8575 Spearman: 0.8571 2024-05-20 16:29:12 - Dot-Product-Similarity: Pearson: 0.8624 Spearman: 0.8662 ``` Student: ``` 2024-05-20 16:29:12 - Student Performance: 2024-05-20 16:29:12 - EmbeddingSimilarityEvaluator: Evaluating the model on the sts-dev dataset: 2024-05-20 16:29:17 - Cosine-Similarity : Pearson: 0.8561 Spearman: 0.8597 2024-05-20 16:29:17 - Manhattan-Distance: Pearson: 0.8569 Spearman: 0.8567 2024-05-20 16:29:17 - Euclidean-Distance: Pearson: 0.8575 Spearman: 0.8571 2024-05-20 16:29:17 - Dot-Product-Similarity: Pearson: 0.8624 Spearman: 0.8662 ```